Database like alignment based on coordinate labels that smoothly
handles missing values: x,y=xr.align(x,y,join='outer').

Keep track of arbitrary metadata in the form of a Python dictionary:
x.attrs.

pandas provides many of these features, but it does not make use of dimension
names, and its core data structures are fixed dimensional arrays.

The N-dimensional nature of xarray’s data structures makes it suitable for dealing
with multi-dimensional scientific data, and its use of dimension names
instead of axis labels (dim='time' instead of axis=0) makes such
arrays much more manageable than the raw numpy ndarray: with xarray, you don’t
need to keep track of the order of arrays dimensions or insert dummy dimensions
(e.g., np.newaxis) to align arrays.

xarray has two core data structures. Both are fundamentally N-dimensional:

DataArray is our implementation of a labeled, N-dimensional
array. It is an N-D generalization of a pandas.Series. The name
DataArray itself is borrowed from Fernando Perez’s datarray project,
which prototyped a similar data structure.

Dataset is a multi-dimensional, in-memory array database.
It is a dict-like container of DataArray objects aligned along any number of
shared dimensions, and serves a similar purpose in xarray to the
pandas.DataFrame.

The value of attaching labels to numpy’s numpy.ndarray may be
fairly obvious, but the dataset may need more motivation.

The power of the dataset over a plain dictionary is that, in addition to
pulling out arrays by name, it is possible to select or combine data along a
dimension across all arrays simultaneously. Like a
DataFrame, datasets facilitate array operations with
heterogeneous data – the difference is that the arrays in a dataset can not
only have different data types, but can also have different numbers of
dimensions.

This data model is borrowed from the netCDF file format, which also provides
xarray with a natural and portable serialization format. NetCDF is very popular
in the geosciences, and there are existing libraries for reading and writing
netCDF in many programming languages, including Python.

xarray distinguishes itself from many tools for working with netCDF data
in-so-far as it provides data structures for in-memory analytics that both
utilize and preserve labels. You only need to do the tedious work of adding
metadata once, not every time you save a file.

pandas excels at working with tabular data. That suffices for many statistical
analyses, but physical scientists rely on N-dimensional arrays – which is
where xarray comes in.

xarray aims to provide a data analysis toolkit as powerful as pandas but
designed for working with homogeneous N-dimensional arrays
instead of tabular data. When possible, we copy the pandas API and rely on
pandas’s highly optimized internals (in particular, for fast indexing).

Importantly, xarray has robust support for converting its objects to and
from a numpy ndarray or a pandas DataFrame or Series, providing
compatibility with the full PyData ecosystem.

Our target audience is anyone who needs N-dimensional labeled arrays, but we
are particularly focused on the data analysis needs of physical scientists –
especially geoscientists who already know and love netCDF.